Detecting Process Anomalies in the GMAW Process by Acoustic Sensing with a Convolutional Neural Network (CNN) for Classification

Today, the quality of welded seams is often examined off-line with either destructive or non-destructive testing. These test procedures are time-consuming and therefore costly. This is especially true if the welds are not welded accurately due to process anomalies. In manual welding, experienced welders are able to detect process anomalies by listening to the sound of the welding process. In this paper, an approach to transfer the “hearing” of an experienced welder into an automated testing process is presented. An acoustic measuring device for recording audible sound is installed for this purpose on a fully automated welding fixture. The processing of the sound information by means of machine learning methods enables in-line process control. Existing research results until now show that the arc is the main sound source. However, both the outflow of the shielding gas and the wire feed emit sound information. Other investigations describe welding irregularities by evaluating and assessing existing sound recordings. Descriptive analysis was performed to find a connection between certain sound patterns and welding irregularities. Recent contributions have used machine learning to identify the degree of welding penetration. The basic assumption of the presented investigations is that process anomalies are the cause of welding irregularities. The focus was on detecting deviating shielding gas flow rates based on audio recordings, processed by a convolutional neural network (CNN). After adjusting the hyperparameters of the CNN it was capable of distinguishing between different flow rates of shielding gas